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ABSTRACT
Lipases are widely distributed in plants, animals, insects and microorganisms. Microbial lipases’ ability to catalyze a wide range of reactions in aqueous and non-aqueous media makes it one of the most important biocatalyst. They have several applications in numerous industries. Lipase can be produced via submerged fermentation (SmF) which utilizes tri-substrate (Cocount pulp, Banana peel and Pineappl peel). The production of Lipase via submerged fermentation is affected by several factors which can be grouped as nutritional factors and physical factors. Box-Behnken design was used to study the effect of castor oil, jatropha oil and olive oil on the production of lipase from tri-substrte using Aspergillus niger. Optimization was carried out using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). A quadratic model was obtained to predict the concentration of lipase as a function of castor oil, jatropha oil, and olive oil from RSM. While for ANN, incremental back propagation (GA) with Hyperbolic-Tangent as the transfer function for both hidden and output layers was the most accurate learning algorithm for predicting lipase production. The performance of both models was evaluated based on their R2 and Root Mean Square Error (RMSE) values. R2 and RMSE values were (0.9702 and 16.42) and (0.98349 and 12.06) for RSM and ANN respectively. Optimization studies produced maximum Lipase concentration of 250.842 U/ml and 253.7875 U/ml for RSM and ANN respectively. ANN was considered to be more efficient because of its lower optimum level of coconut pulp, banan peel and pineapple peel and higher optimized yield. At maximum lipase concentration of 253.7875 U/ml, the optimum level of factors was 1.0509793 % w/w of coconut pulp, 1.9999078 % w/w of banana peel and 1.999924 % w/w of pineapple peel. It was therefore concluded that ANN is better than RSM in the modelling and optimization of the effects of coconut pulp, banana peel and pineapple peel on lipase productions